In 2008, approximately 1.9% of the U.S. population reported some form of paralysis resulting in difficulty or inability to move their arms or legs. Of those, 23% reported being paralyzed due to a spinal cord injury (SCI). There are approximately 12,000 new SCI cases each year. According to The University of Alabama National Spinal Cord Injury Statistical Center and the Centers for Disease Control and Prevention (CDC), the cost of living with SCI can be considerable, and also may vary greatly due to the severity of injury. One recent estimate indicates that SCI alone costs roughly $40.5 billion annually. Providing a system or a method of safe and reliable ambulation and other body movements to these patients would improve quality of life for these patients as well as save on future direct and indirect associated lifetime costs, thereby reducing the socio-economic burden of disability in the US. Moreover, cerebrovascular diseases, or strokes, affect approximately 795,000 people every year in the United States alone, and according to the Survey of Income and Program Participation (SIPP, a survey of the US Bureau of the Census), strokes are a leading cause of serious, long-term disability. With at least 50% of survivors experiencing some hemiparesis, strokes account for the poor physical health and the social dysfunction evident in survivors. Therefore, increasing access to neuro-rehabilitation would consequently increase functional recovery and long-term quality of life (QOL) in these patients, while allowing them greater participation in society. Harnessing brain—machine interfaces (BMI) and robotic-assisted rehabilitation technologies has the potential not only to promote functional restitution through sensorimotor adaptation and central nervous system plasticity, but also to help reduce the socio-economic burden of such disabilities. By adjusting parameters tailored to each individual, his/her state of disability, and goals of intervention, these technologies can provide greater durations of consistent, patient-engaged, repetitive motor practice that consequently allow a physical therapist to work with more patients in the same allotted time. Moreover, BMIs can also be used as a method to measure functional recovery and neuronal plastic changes. Accordingly, there is a need in the art for improved movement assistance systems and biological interface apparatuses to adequately serve these patient populations.
There are a variety of robotic technologies providing robotic exoskeletons or the like. For example, some robotic exoskeletons utilize sensors, such as on a patient's skin, to anticipate desired movements. Other robotic exoskeletons require a patient with a functioning upper body (e.g. hands, arms, and shoulders). Additional robotic exoskeletons utilize external controllers, such as remote controller. In some cases, invasive methods may be utilized to implant neural interfaces utilized for controlling robotic exoskeletons if the benefit-risk ratio is favorable given the surgical risks and complications (e.g., infections or malfunction of the implanted devices) of invasive technologies.
Systems and methods discussed herein may utilize non-invasive scalp electroencephalography (EEG) to acquire neural signals. The neural signals may be provided to a brain machine interface (BMI) that decodes the neural signals into desired motions for an exoskeleton, or to control the movements of a virtual avatar in a motor rehabilitation context or in a gaming application.